Master thesis: Controlling industrial robots with gener-ative AI

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Problem and objective

Large Language Models (LLMs) are experienc-
ing a significant surge in popularity. Following

the release of ChatGPT, their adoption has dra-
matically increased, with a broad range of users

reporting widespread usage and practical ben-
efits. By leveraging Generative Artificial Intelli-
gence (GenAI) in work environments, productiv-
ity can be enhanced, and results improved.

While most tasks utilize LLMs‘ extensive gen-
eral knowledge, these models can be fine-tuned

to understand and solve complex problems. Ex-
amples include safety monitoring during pro-
duction or generative design for improved ma-
terial usage. Therefore, LLMs and GenAI have

the potential to revolutionize entire industries.
LLMs can generate multiple types of content,
such as images, text, or source code. A notable
example of the utilization of LLMs for source
code generation is the GitHub Copilot, which

significantly impacted the field of software de-

Source code generation can also be used to
control industrial robots, like ABBs YuMi series.

Currently, programming such robots requires
expert knowledge of the robot’s mechanics and
a translation of the desired movements into
code. This creates a barrier to using robots due

to high training and implementation costs. Con-
trolling the robot via natural language would sig-
nificantly lower this barrier. However, it remains

unclear whether and how LLMs can effectively
solve this problem.
To address this uncertainty, the main objective
of this thesis is to develop a concept for robot

control using LLMs to translate natural lan-
guage inputs into suitable code for the robot.

This includes a comprehensive analysis of ex-
isting LLM-based approaches for source code

generation, evaluating the capabilities of LLMs
for robot control, developing a concept for robot

control using LLMs, designing a reference ar-
chitecture, and implementing a proof of con-
cept. Furthermore, the developed approach will

be generalized and abstracted into a generic

methodology that can be reused for future pro-

Work program
1. Familiarization with the task and
preparation of a detailed outline

130 h

2. Problem analysis to determine re-
quirements for the approach

150 h
3. Analysis of the state of the art 120 h
5. Development and validation of a
systematic approach
• Identification of relevant
communication interfaces
• Selection of suitable training
• Tuning of the selected LLM

• Implementation and valida-
tion of the approach

200 h

6. Documentation of the results 130 h
7. Final thesis presentation 20 h
750 h

The times planned in the work program are
standard values.

M.Sc. Benjamin Tiggemann
M.Sc. Ruslan Bernijazov
Heinz Nixdorf Institut
Universität Paderborn
Fürstenallee 11, 33102 Paderborn

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